Apple Inc.

Amazon Technologies, Inc.

Michael Reese Bastian

Stephen Olivier Lemay

Timothy S Paek

Brandon J Newendorp

Srikrishna Sridhar

Shawn R Scully

Charles Maalouf

Minwoo Jeong

Heriberto Nieto

Joanna Peterson

Brandt M Westing

Shiwen Zhao

Roman Holenstein

Ariya Rastrow

Bjorn Hoffmeister

Baiyang Liu

Sankaran Panchapagesan

Systems and processes for operating an intelligent automated assistant are provided. An example process includes detecting input representing motion of an electronic device and sampling an audio input with a microphone of the electronic device. The example process further includes determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session. In accordance with a determination to initiate the virtual assistant session, the example process includes initiating the virtual assistant session. In accordance with a determination not to initiate the virtual assistant session, the example process includes forgoing initiating the virtual assistant session.

Raise to speak

Features are disclosed for error tolerant model compression. Such features could be used to reduce the size of a deep neural network model including several hidden node layers. The size reduction in an error tolerant fashion ensures predictive applications relying on the model do not experience performance degradation due to model compression. Such predictive applications include automatic recognition of speech, image recognition, and recommendation engines. Partially quantized models are re-trained such that any degradation of accuracy is 'trained out' of the model providing improved error tolerance with compression.

Error tolerant neural network model compression

Growing community of inventors

Bothell, WA, United States of America

Michael Reese Bastian